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Expert Systems An Overview of Expert Systems Expert Systems TOPICS The nature of expertise • Who is an Expert, and Why? The Characteristics of an Expert Systems • What Makes it different and Why ? Additional Issues in Expert Systems • Knowledge acquisition (Building knowledge bases) • Knowledge assessment • Explanation facilities Expert Systems The Nature of Expertise Assumes a highly specialized set of Skills • NOT just general knowledge Assumes a very specialized problem domain • Analogous to our previous ‘Forest vs. Tree’ Idea Assumes logic, problem solving and experience • NOT simple intuition or indefinable behaviors Expert Systems The Nature of Expertise Performance Who is an Expert?? • That is NOT an easy Question • There are many practitioner but very few experts Expertise • Notice that just because you have experience, that does NOT mean that you are an expert Characteristics of Experts • Fast, ACCURATE, problem Solving • Pattern Recognition • Use of Heuristics – Based on past experience • Scarcity Expert Systems The Nature of Expertise Necessary Expert Traits • Be Recognized as an Expert • Know how they perform the task • Can NOT just act intuitively without being able to explain their behaviors • Have the time and ability to explain how they perform • Be Motivated to Cooperate Expert Systems The Nature of Expertise How do you know who is an expert?? • Also NOT an easy Question, although some are obvious • There are references, However (a few off the Internet): • ExpertPages.com: A directory for legal professionals in search of experts, expert witnesses, or consultants. Search by state, country, or subject area. http://www.expertpages.com/ • Experts Directory A searchable directory of experts from the legal, medical, journalism and other professions. http://www.experts.com Are they really Experts ??? Don’t Mortgage the House! Expert Systems Expert System Characteristics “An expert system is a computer program that represents and reasons with knowledge of some specialist subject with a view to solving problems or giving advice.” Jackson (1999) Turing Test • A computer program demonstrates artificial intelligence if it can “pass’ as a human (c. 1950) 1912-54 • In 1990, the Cambridge Center for Behavioral Studies began offering the $100,000 Loebner Prize to the first program whose responses were indistinguishable from a human’s (No one has ever won) Expert Systems Expert System Characteristics • Gary Kasparov vs. IBM’s Deep Blue • May 11, 1997 • Garry Kasparov resigned 19 moves into Game 6 • Deep Blue wins the Best of Six game series 3.5 to 2.5 • IBM Development Team wins $700,000 • Kasparov wins $400,000 • The first win by a computer program over an International Grand Master since man/computer games were first began in 1970 Expert Systems Expert System Characteristics Basic Requirements • simulates human reasoning • Rule/Heuristic Based: Rule: If there is a potato in the tailpipe, the car will not start. Finding: There is a potato in the tailpipe. Conclusion: The car will not start. (Truth preserving inference) Rule: If there is a potato in the tailpipe, the car will not start. Finding: My car will not start. Conclusion: Therefore, there is a potato in the tailpipe. (Non-Truth preserving inference) Expert Systems Expert System Characteristics Basic Requirements • simulates human reasoning • Inference Engines • The ‘Driving’ Force in an Expert System • Reasons with any rule constructed via rule set manager • Searches for applicable rules • Evaluates the predicates of those rules to determine their “truth” • Executes the actions specified in “fired” (activated) rules Expert Systems Expert System Characteristics Basic Requirements • simulates human reasoning • Inference Engines • Forward Chaining • Corresponds to the idea of Deductive reasoning Theory Birds can Fly Hypothesis Ostriches Can Fly Observation OK – I was wrong ! Rejection (I Fly to Australia) Confirmation Expert Systems Expert System Characteristics Basic Requirements • simulates human reasoning • Inference Engines • Forward Chaining • Corresponds to the idea of Deductive reasoning • Consists of a condition part and an action part • Conditions (rules) are matched against the database • If true, the action is fired • The forward chaining engine cycles repeatedly until it runs out of rules or a rule instructs it to stop. Expert Systems Expert System Characteristics Basic Requirements • simulates human reasoning • Inference Engines • Forward Chaining • Backward Chaining • Corresponds to the idea of Inductive reasoning Theory Ostriches Can’t Fly (what a Moron I was!) Not all Birds can Fly Tentative Hypothesis Pattern Observation Birds Flying, but no Ostriches I’m back in The Australian Outback – Bird watching Expert Systems Expert System Characteristics Basic Requirements • simulates human reasoning • Inference Engines • Forward Chaining • Backward Chaining • Corresponds to the idea of Inductive reasoning • Involves trying to prove a given goal by using rules to generate sub-goals and recursively trying to satisfy them. • The engine looks at conclusions and determines all rules that could reach that conclusion • Each rule is then examined for its premises • If true, the rule is fired and a value is established • The process continues until all possible solutions are generated Expert Systems Expert System Characteristics Basic Requirements • simulates human reasoning • Knowledge Representation • Knowledge Bases • A repository (Database) of data and metadata • Contains all the Rules established by the manager • The data are stored as objects, which can be fired as needed • Includes Symbolic data • Includes Relationships between data • May be used in conjunction with a standard database Expert Systems Expert System Characteristics Basic Requirements • • • • simulates human reasoning Knowledge Representation Deal with realistically complex Problems Reach Multiple Conclusions • Especially as a result of backward chaining • Explain the conclusions reached • The logic used must be demonstratable • Deal with Missing Information • “Fuzzy Logic” • Non-numerical Analysis • Demonstrate High Performance • Should approximate the performance of the expert Expert Systems Expert System Characteristics Basic Requirements ES Components User Interface Inference Engine Database ES Shell A rule engine and scripting Environment Knowledge Base Expert Systems Expert System Characteristics Basic Requirements ES Components Differences Between ES and DSS Expert Systems • Based On Expert • Based on Logical Reasoning Decision Support Systems • No Experts Available • System Questions User • Used Frequently • Based on Numerical Analysis • User Questions System • Used for Ad-hoc Problems • • • • • • • • Final Solution(s) Provided Very Accurate Multiple Solutions Learning Possible Outputs provided based Analysis Unknown Accuracy Always the same output Always the same output Expert Systems Additional Topics Knowledge Acquisition “The transfer and transformation of potential problem-solving expertise from some knowledge source to a program” - Buchanan et al. (1983) • Transfer of the Expert’s Knowledge as a set of rules into the Knowledge Base • Since the Expert is not expected to code the rules, a Knowledge Engineer is required • lengthy & intense interviews Required • slow (2 to 5 units of knowledge /day) ??? Why ??? • Imprecise, illogical, jargon or colloquialisms, experience, contextual detail, reliability of sources, ... Expert Systems Additional Topics Knowledge Acquisition • Example: How to find a forgotten Password: Expert (Computer Center Guru): Well, if it’s a YP password, I first log on as root on the YP master KE: (Knowledge Engineer): Er, what’s the YP master? Expert: It’s the diskful machine that contains a database of network information KE: ‘Diskful’ meaning - ? Expert: -it has the OS installed on local disk KE: Ah. (scribbles furiously) So you log on… Expert: As root. Then I edit the password datafile, remove the encrypted entry, and make the new password map... This is the weakest link in the process !! Expert Systems Additional Topics Knowledge Acquisition • Potential Solutions/Problems • automated knowledge elicitation • interactive programs/automated conversation • Problem: There are no Good Programs available (yet) • textual scanning • Parsing of conversations to extract the important components • Problem: NLP is still in its infancy • machine learning • deriving decision rules from examples • evaluating / weighting rules • performance optimization of rules • Problem: Only Limited Success to date I don’t get it ! Me Neither Expert Systems Additional Topics Knowledge Acquisition Knowledge Assessment • logical adequacy • sound & complete inferencing • heuristic Power • efficiency Vs. optimality (Effectiveness) • notational Convenience • How accurately do the rules reflect the logic? Expert Systems Additional Topics Knowledge Acquisition Knowledge Assessment Explanation Facility • Necessary to check validity of Solutions • The Chain of reasoning must be logged • Solution Accountability must be determined • Deficiencies must be corrected Expert Systems Additional Topics Knowledge Acquisition Knowledge Assessment Explanation Facility Available Packages/Tools • Symbolic Manipulation Languages • LISP (LISt Processor) • Prolog • Expert Shells • CLIPS (Free Download: http://www.ghg.net/clips/CLIPS.html) • Jess (Free Download: http://herzberg.ca.sandia.gov/jess/ ) • Others: A good list can be found at http://www-2.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/expert/systems/0.html Expert Systems